agentic-detection-lookups
Machine-readable detection lookups for SIEM enrichment and AI agents. Query 800+ LOLBAS and GTFOBins binaries plus process parent-child baselines — get risk levels, abuse categories, and MITRE ATT\&CK mappings without embedding data in prompts.
README
Agentic Detection Lookups
Machine-readable detection lookups for SIEM enrichment and AI agents. MCP-native.
Stop regex-matching 200+ binaries. Enrich in one
match()call.
Feed it to your SIEM, your SOAR, your agent, or your LLM.
What is this?
A collection of structured CSV lookup files purpose-built for:
- SIEM enrichment — one
match()/lookup/joinreplaces entire rule categories - AI agent tooling — MCP server included, agents query detection context in real-time
- Detection automation — consistent schema, CI-updated, deploy-ready
Lookup Files
| File | Entries | OS | Description |
|---|---|---|---|
lolbas_binaries.csv |
232 | Windows | Living Off The Land Binaries and Scripts — risk-scored, categorized, MITRE-mapped |
gtfobins.csv |
477 | Linux | GTFOBins Unix binaries — shell escape, priv-esc, file ops, MITRE-mapped |
parent_child_baselines.csv |
97 | Both | Expected/suspicious process parent→child relationships for Windows and Linux |
Schema Contract
Every lookup file follows:
- First column = match key (the field you join on)
- Always includes
riskorrisk_if_unexpectedcolumn - Always includes MITRE ATT&CK technique mapping
- No nested data — flat columns, pipe-delimited for multi-value
- UTF-8, no BOM, Unix line endings, header row always present
Quick Start
SIEM (copy-paste)
CrowdStrike NG-SIEM:
#event_simpleName=ProcessRollup2
| binary := lower(FileName)
| match(file="lolbas_binaries.csv", field=binary, column=filename, include=[categories, mitre_ids, risk])
| risk="high"
Splunk:
index=crowdstrike event_simpleName=ProcessRollup2
| rex field=FileName "(?<binary>[^\\\\]+)$"
| lookup lolbas_binaries.csv filename AS binary OUTPUT categories mitre_ids risk
| where risk="high"
Elastic (ES|QL):
FROM logs-endpoint.events.process-*
| WHERE event.action == "start"
| ENRICH lolbas-policy ON process.name = filename WITH categories, risk
| WHERE risk == "high"
Microsoft Sentinel:
DeviceProcessEvents
| extend binary = tolower(FileName)
| join kind=inner (_GetWatchlist('lolbas_binaries')) on $left.binary == $right.filename
| where risk == "high"
See queries/ for full query libraries per platform.
MCP Server (AI agents)
{
"servers": {
"detection-lookups": {
"type": "stdio",
"command": "python",
"args": ["-m", "mcp_server"],
"cwd": "/path/to/agentic-detection-lookups"
}
}
}
Then your agent can:
→ detection_lookup_binary("certutil.exe")
← {source: "lolbas", risk: "medium", categories: ["Download"], mitre_ids: ["T1105"]}
→ detection_lookup_binary("python")
← {source: "gtfobins", risk: "high", categories: ["shell", "reverse-shell", ...], mitre_ids: ["T1059"]}
→ detection_check_parent_child("winword.exe", "cmd.exe")
← {expected: false, risk_if_unexpected: "critical", mitre_id: "T1204.002"}
MCP Tools
| Tool | Input | Output |
|---|---|---|
detection_lookup_binary |
filename | Risk, categories, MITRE IDs, source (lolbas/gtfobins) |
detection_check_parent_child |
parent, child, os_filter | Expected/suspicious, risk level, triage guidance |
detection_list_by_category |
category, limit, offset | Paginated binaries in that abuse category (cross-platform) |
detection_list_by_mitre |
technique_id, limit, offset | Paginated binaries mapped to that technique (cross-platform) |
detection_search |
query, limit | Matches across all lookup data with total/has_more |
detection_list_lookups |
— | All files with row counts and columns |
Data Sources
| Lookup | Source | Update Frequency |
|---|---|---|
| LOLBAS binaries | LOLBAS Project | Weekly (automated) |
Installation
Prerequisites
- Python 3.10+
- VS Code with GitHub Copilot (for MCP integration)
Install
git clone https://github.com/detection-forge/agentic-detection-lookups.git
cd agentic-detection-lookups
python -m venv .venv
# Windows:
.venv\Scripts\activate
# Linux/macOS:
source .venv/bin/activate
pip install -e .
Configure MCP Client (VS Code)
Add to your VS Code User settings (Ctrl+Shift+P → "Preferences: Open User Settings (JSON)") or ~/.vscode/mcp.json:
{
"servers": {
"detection-lookups": {
"type": "stdio",
"command": "/absolute/path/to/.venv/bin/python",
"args": ["-m", "mcp_server"],
"cwd": "/absolute/path/to/agentic-detection-lookups"
}
}
}
Windows example:
{ "servers": { "detection-lookups": { "type": "stdio", "command": "C:\\Code\\.venv\\Scripts\\python.exe", "args": ["-m", "mcp_server"], "cwd": "C:\\Code\\agentic-detection-lookups" } } }
Reload VS Code: Ctrl+Shift+P → "Reload Window"
Verify
In Copilot Chat (Agent mode):
Is certutil.exe a LOLBAS binary?
✅ Returns risk, categories, and MITRE mappings = working!
Run standalone (CLI)
detection-lookups
This starts the MCP server on stdio transport (useful for piping JSON-RPC or connecting other MCP clients).
Upload to your SIEM
- CrowdStrike NG-SIEM: Upload via API or UI (Settings → Lookup Files)
- Splunk: Settings → Lookups → Lookup table files → Add new
- Elastic: Create enrich index + ingest pipeline
- Sentinel: Configuration → Watchlist → Add new
Project Structure
agentic-detection-lookups/
├── lookups/ # The data (CSV files)
│ ├── lolbas_binaries.csv
│ ├── gtfobins.csv
│ └── parent_child_baselines.csv
├── queries/ # Copy-paste detection queries
│ ├── crowdstrike_ngsiem.md
│ ├── splunk.md
│ ├── elastic.md
│ └── microsoft_sentinel.md
├── mcp_server/ # MCP server for AI agents
│ ├── server.py
│ └── __init__.py
├── scripts/ # Update/maintenance scripts
├── LICENSE # Apache 2.0
├── NOTICE
└── pyproject.toml
Contributing
PRs welcome. See CONTRIBUTING.md for guidelines.
To add a new lookup file:
- Follow the schema contract (match key first, include risk + MITRE columns)
- Include at least one query example per SIEM platform
- Add a tool to the MCP server
License
Apache 2.0 — See LICENSE and NOTICE.
Built by Gene Kazimiarovich | Part of Detection Forge
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。